#llm + #security

We did not enable internet access or inter-agent communication for many of the environments in these training experiments. Despite these restrictions, the agents discovered ways to exploit our research infrastructure to communicate with one another and access the internet.

The models first found ways to communicate by writing files into the Artifactory package manager. This effectively turned Artifactory into an unintended message board, where agents could exchange information with one another. Read more: How agents used the message board 

The agents also found ways to get access to the internet. Using an exploit⁠(opens in a new window), the agents made Artifactory, which itself had access to the internet for the limited purpose of downloading and installing packages, send any internet requests on their behalf. After the agents discovered these methods for obtaining internet access, they also shared them with other agents through the message board, which provided information to enable additional agents to exploit our infrastructure.

• One worm took twelve unrelated organisations between 09:35 and 13:18 UTC on 4 August 2026, each republishing its whole namespace within seconds.

• The confirmed footprint is 2,234 poisoned versions across 444 package names. latest still resolves to a poisoned version on most affected names, so upgrading is not a fix. The maintainers have since cleaned the keyv and cacheable family and unpublished its poisoned releases, which does not help a lockfile pinned while they were live.

• Every poisoned release adds "preinstall": "node setup.mjs", which runs on a bare npm install. Disable install scripts in CI, and treat any npm token used in CI during the window as compromised.

• The payload steals GitHub, npm, AWS, GCP, Azure, Stripe, Vault and Kubernetes credentials, scrapes GitHub Actions runner memory, and can republish packages with valid SLSA provenance.

• It installs a dead-man’s switch that polls GitHub with the stolen token and executes a handler when that token is revoked. Hunt it before rotating anything, because rotation is the trigger.

• Poisoned packages include the auth libraries @or-sdk/auth, @or-sdk/api-tokens, @or-sdk/permissions and @arv-bedrock/auth. The @ornikar packages are mostly lint and build configs, which land on CI runners and developer laptops as dev dependencies.

• There is no C2 host. Exfiltration rides GitHub dead-drop repositories and Actions artifacts under RSA-4096 encryption, and stage one pulls its runtime from the official Bun releases, so no attacker domain appears at either stage.

• 546 dead-drop repositories described Shai-Hulud: Here We Go Again appeared on 4 August 2026, and the same marker appears on the TanStack compromise dated 11 May 2026.

• The keyv repository also ships .claude/settings.json and .vscode/tasks.json hooks that execute when the project opens in an IDE or agent session. Both are still on main.

• The commit that added those hooks shows as verified by GitHub with its author spoofed to github-actions[bot], and the poisoned keyv release carries intact OIDC and SLSA attestation. Trust neither signal here.

1Password for Claude lets you authorize Claude to complete browser-based tasks that require an account login. This is useful for things like booking travel or managing online accounts.

According to 1Password, approved credentials are delivered through a secure channel and injected directly into the destination page. The password, one-time code, and other secrets never enter Claude’s context, memory, or Anthropic’s systems.

Instead of granting ongoing access to a vault, Claude requests the specific login items it needs for a task. The user can approve or deny that request with a biometric prompt, and the permission lasts only for the current session.

1Password can also broker access across multiple websites during the same task, allowing Claude to complete a multi-step workflow without stopping for a new login each time.

The launch also introduces what 1Password calls Agentic Mode.

When a compatible AI agent takes control of the browser, the 1Password extension automatically locks down the vault so that only the credentials explicitly approved for that task remain available.

It requires the 1Password desktop app and browser extension, along with the Claude desktop app and browser extension.

Support for payment cards and identity information is planned for a later update.

The Anthropic partnership was first outlined in March, when 1Password said Claude would gain consent-based access to vault items.

xAI's grok CLI tool faced severe community backlash yesterday when it became apparent that running the command in a directory could upload that entire directory to xAI's Google Cloud buckets. One user reported running it in their home directory and seeing it upload "my SSH keys, my password manager database, my documents, photos, videos, everything".

Key Findings

An LLM agent executed the post-compromise actions in real time rather than running a pre-built playbook. This is the first AI-agent-driven intrusion the Sysdig TRT has captured. The full attack chain — marimo notebook compromise to internal Postgres database dump — ran end-to-end in under one hour. The SSH bastion phase exfiltrated the Postgres schema and full contents of an internal database in less than two minutes. Cloudflare Workers were used as a per-request egress pool: 12 cloud API calls fanned across eleven distinct IPs in 22 seconds, defeating per-source-IP detection.

The OWASP GenAI Security Project is a global, open-source initiative dedicated to identifying, mitigating, and documenting security and safety risks associated with generative AI technologies, including large language models (LLMs), agentic AI systems, and AI-driven applications.

At Lasso, we have been building Intent Security, a runtime security framework that ensures every component in the agentic system behaves as intended. It monitors the behavior of each component and analyzes their alignment. Like auto mode, when alignment holds it allows actions to proceed. When misalignment is detected, it intervenes. When we read Anthropic's post, the overlap in core assumptions was hard to miss. This post provides a comparison of the two approaches.

Independent evaluation without cross-contamination is what enables misalignment detection.

‍Anthropic's input layer screens external content for injection attempts before it reaches the agent to determine whether tool outputs are safe. The output layer structurally evaluates whether the agent's tool calls are aligned with user intent. Critically, the output classifier never sees tool results, to prevent compromised external content from influencing the security decision.

For most organizations, autoMode.environment is the only field you need to set. It tells the classifier which repos, buckets, and domains are trusted: the classifier uses it to decide what “external” means, so any destination not listed is a potential exfiltration target. The default environment list trusts the working repo and its configured remotes. To add your own entries alongside that default, include the literal string "$defaults" in the array. The default entries are spliced in at that position, so your custom entries can go before or after them.

Users have two solutions for avoiding this fatigue: a built-in sandbox where tools are isolated to prevent dangerous actions, or the --dangerously-skip-permissions flag that disables all permission prompts and lets Claude act freely, which is unsafe in most situations. Figure 1 lays out the tradeoff space. Sandboxing is safe but high-maintenance: each new capability needs configuring, and anything requiring network or host access breaks isolation.

Auto mode uses two layers of defense: one for what Claude reads, one for what Claude does.

a server-side prompt-injection probe scans tool outputs:

At the input layer, a server-side prompt-injection probe scans tool outputs (file reads, web fetches, shell output, external tool responses) before they enter the agent's context. When content looks like an attempt to hijack behavior, the probe adds a warning to the agent's context before the result is passed along—telling the agent to treat this content as suspect and anchor on what the user actually asked for.

At the output layer, the transcript classifier (running on Sonnet 4.6) evaluates each action against a set of decision criteria before it executes, acting as a substitute for a human approver. The classifier runs in two stages: a fast single-token filter (deciding ‘yes’ to block or ‘no’ to allow), followed by chain-of-thought reasoning only if the first filter flags the transcript. Because most actions clear the first stage, reasoning tokens are spent only where needed. The classifier sees only user messages and the agent's tool calls; we strip out Claude's own messages and tool outputs, making it reasoning-blind by design.

So... Claude Code's auto mode lets through 17% of dangerous real world actions. From the article:

False-negative rate (FNR) is the fraction of dangerous actions the classifier lets through... The 17% false-negative rate on real overeager actions is the honest number. The agent was trying to solve the user's problem and tried to execute a dangerous command past what was authorized.

I don't think comparing it to --dangerously-skip-permissions is intellectually honest since Claude Code now prompts you to make Auto Mode your default mode now, while --dangerously-skip-permissions was basically a hidden feature. I don't know anyone else that I work with who even knew about it.

Whether 17% is acceptable depends on what you're comparing against. If you are running --dangerously-skip-permissions, this is a substantial improvement. If you are manually approving every action carefully, it's arguably a regression—you're trading your own judgment for a classifier that will sometimes make a mistake. Auto mode is meant for the first group, and for tasks where the second group's approval overhead isn't worth the marginal safety. It is not a drop-in replacement for careful human review on high-stakes infrastructure.

At the input layer, a server-side prompt-injection probe scans tool outputs (file reads, web fetches, shell output, external tool responses) before they enter the agent's context. When content looks like an attempt to hijack behavior, the probe adds a warning to the agent's context before the result is passed along—telling the agent to treat this content as suspect and anchor on what the user actually asked for.

Manus Sandbox is a fully isolated cloud virtual machine that Manus allocates for each task. Each Sandbox runs in its own environment, does not affect other tasks, and can execute in parallel. The power of Sandbox lies in its completeness—just like the personal computer you use, it has full capabilities: networking, file system, browser, various software tools. Our AI Agent has been designed and trained to effectively choose and correctly use these tools to help you complete tasks. Moreover, with this computer, the AI can solve problems through what it does best—writing code—and can even help you create complete websites and mobile apps. All of this happens on the virtualization platform behind Manus. These Sandboxes can work 24/7 to complete the tasks you assign without consuming your local resources.

What's in Your Sandbox Your Manus Sandbox stores the files needed during task execution, including: Attachments uploaded by you Files and artifacts created and written by Manus during execution Configurations needed by Manus to execute specific tasks (such as tokens uploaded by users, or tokens assigned by Manus to users for calling related APIs) You can view all artifact files in the Sandbox via the "View all files in this task" entry in the top-right corner.

Somebody realized that Claude would try unavailable NPX commands for packages that didn’t exist so he started publishing the packages so we could run. What do you want other machines.

generally considered the foundational academic work on indirect prompt injection. It's been reproduced against virtually every major agentic system since.

Website: https://sites.google.com/view/invitation-is-all-you-need

The growing integration of LLMs into applications has introduced new security risks, notably known as Promptware—maliciously engineered prompts designed to manipulate LLMs to compromise the CIA triad of these applications. While prior research warned about a potential shift in the threat landscape for LLM-powered applications, the risk posed by Promptware is frequently perceived as low. In this paper, we investigate the risk Promptware poses to users of Gemini-powered assistants (web application, mobile application, and Google Assistant).

Our analysis focuses on a new variant of Promptware called Targeted Promptware Attacks, which leverage indirect prompt injection via common user interactions such as emails, calendar invitations, and shared documents. We demonstrate 14 attack scenarios applied against Gemini-powered assistants across five identified threat classes: Short-term Context Poisoning, Permanent Memory Poisoning, Tool Misuse, Automatic Agent Invocation, and Automatic App Invocation. These attacks highlight both digital and physical consequences, including spamming, phishing, disinformation campaigns, data exfiltration, unapproved user video streaming, and control of home automation devices

Over the course of our work, we deployed multiple layered defenses, including: enhanced user confirmations for sensitive actions; robust URL handling with sanitization and Trust Level Policies; and advanced prompt injection detection using content classifiers - Google

An MCP Gateway for LLM‘s that applies policies to the actions and stores credential separately from the LLM

Two days ago, Anthropic released the Claude Cowork research preview (a general-purpose AI agent to help anyone with their day-to-day work). In this article, we demonstrate how attackers can exfiltrate user files from Cowork by exploiting an unremediated vulnerability in Claude’s coding environment, which now extends to Cowork. The vulnerability was first identified in Claude.ai chat before Cowork existed by Johann Rehberger, who disclosed the vulnerability — it was acknowledged but not remediated by Anthropic.

1. The victim connects Cowork to a local folder containing confidential real estate files
2. The victim uploads a file to Claude that contains a hidden prompt injection
3. The victim asks Cowork to analyze their files using the Real Estate ‘skill’ they uploaded
4. The injection manipulates Cowork to upload files to the attacker’s Anthropic account

At no point in this process is human approval required.

One of the key capabilities that Cowork was created for is the ability to interact with one's entire day-to-day work environment. This includes the browser and MCP servers, granting capabilities like sending texts, controlling one's Mac with AppleScripts, etc.

These functionalities make it increasingly likely that the model will process both sensitive and untrusted data sources (which the user does not review manually for injections), making prompt injection an ever-growing attack surface. We urge users to exercise caution when configuring Connectors. Though this article demonstrated an exploit without leveraging Connectors, we believe they represent a major risk surface likely to impact everyday users.